Instructions to use mradermacher/KwaiCoder-AutoThink-preview-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/KwaiCoder-AutoThink-preview-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/KwaiCoder-AutoThink-preview-GGUF", dtype="auto")

llama-cpp-python

How to use mradermacher/KwaiCoder-AutoThink-preview-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/KwaiCoder-AutoThink-preview-GGUF",
	filename="KwaiCoder-AutoThink-preview.IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use mradermacher/KwaiCoder-AutoThink-preview-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/KwaiCoder-AutoThink-preview-GGUF with Ollama:
```
ollama run hf.co/mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/KwaiCoder-AutoThink-preview-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mradermacher/KwaiCoder-AutoThink-preview-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mradermacher/KwaiCoder-AutoThink-preview-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/KwaiCoder-AutoThink-preview-GGUF to start chatting

How to use mradermacher/KwaiCoder-AutoThink-preview-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mradermacher/KwaiCoder-AutoThink-preview-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use mradermacher/KwaiCoder-AutoThink-preview-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/KwaiCoder-AutoThink-preview-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/KwaiCoder-AutoThink-preview-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.KwaiCoder-AutoThink-preview-GGUF-Q4_K_M

List all available models

lemonade list

Thanks for updating the quants after upstream pushed new changes ...

by davidpfarrell - opened Jun 20, 2025

Discussion

davidpfarrell

Jun 20, 2025

•

edited Jun 20, 2025

@davidpfarrell I'm requanting this now. Sorry for the delay. I unfortunately don't think you will ever see this message unless you have email notifications enabled

I did ! Thanks for the updates!

Q: Would you consider updating the card description to clarify that your quants are based on the "source files dated june 6 (uploaded june 10)" or similar ?
Same for your (just discovered) imatrix quants ...

Also, I'm going to re-download now, and think I'll go for your iMatrix Q6_K version - Lemme know if you think I should go with this card's (static?) quant instead?

Thanks again and have a good on!

[edit] s/wants/quants/

nicoboss

Jun 20, 2025

•

edited Jun 20, 2025

I did ! Thanks for the updates!

Awesome. Nice you had them enabled.

Q: Would you consider updating the card description

Due to our automation it probably would just get reverted again the next time we update the model card and so not really worth it. We deleted and recreated the entire repo so all the dates (created at, last updated at and commit date) all point to today making it quite obvious for the user,

Also, I'm going to re-download now, and think I'll go for your iMatrix Q6_K version - Lemme know if you think I should go with this card's (static?) quant instead?

Always go for imatrix quants. They are far better than static quants in every way. I personally usually go for i1-Q5_K_M version. It the smallest quant that in my interpretation of my Perplexity, KL Divergence, Top token probability, Same token probability and eval measurements during Q4 2024 gives indistinguishable results from the original. You probably want to look at the quality column on our model download page under https://hf.tst.eu/model#KwaiCoder-AutoThink-preview-GGUF to get an idea how quants in general rank in terms of quality.

Thanks again and have a good on!

Thanks a lot for letting us know as well.

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